examples/lgt.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LGT Quick Start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGT stands for Local and Global Trend, which is an important model type in orbit package. In the model equation, there is a local trend term and a global trend term.\n",
    "\n",
    "In this notebook we will show how to use Orbit LGT models with the US unemployment claims data.\n",
    "\n",
    "**Note: Negative response values are not allowed in LGT model, due to the existence of the global trend term.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:22.048121Z",
     "start_time": "2021-08-14T22:30:22.028361Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:24.435003Z",
     "start_time": "2021-08-14T22:30:22.049926Z"
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from orbit.models import LGT\n",
    "from orbit.diagnostics.plot import plot_predicted_data, plot_predicted_components\n",
    "from orbit.utils.dataset import load_iclaims\n",
    "\n",
    "from orbit.utils.plot import get_orbit_style\n",
    "plt.style.use(get_orbit_style())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*iclaims_example* is a dataset containing the weekly initial claims for US unemployment benefits against a few related google trend queries (unemploy, filling and job) from Jan 2010 - June 2018. \n",
    "This aims to mimick the dataset from the paper [Predicting the Present with Bayesian Structural Time Series](https://people.ischool.berkeley.edu/~hal/Papers/2013/pred-present-with-bsts.pdf) by SCOTT and VARIAN (2014).\n",
    "\n",
    "Number of claims are obtained from [Federal Reserve Bank of St. Louis](https://fred.stlouisfed.org/series/ICNSA) while google queries are obtained through [Google Trends API](https://trends.google.com/trends/?geo=US).\n",
    "Note that dataset is transformed by natural log before fitting in order to be fitted as a multiplicative model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:24.892583Z",
     "start_time": "2021-08-14T22:30:24.437875Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "week              datetime64[ns]\n",
       "claims                   float64\n",
       "trend.unemploy           float64\n",
       "trend.filling            float64\n",
       "trend.job                float64\n",
       "sp500                    float64\n",
       "vix                      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "df = load_iclaims()\n",
    "\n",
    "# define date and response column\n",
    "DATE_COL = 'week'\n",
    "RESPONSE_COL = 'claims'\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:24.956939Z",
     "start_time": "2021-08-14T22:30:24.896502Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>claims</th>\n",
       "      <th>trend.unemploy</th>\n",
       "      <th>trend.filling</th>\n",
       "      <th>trend.job</th>\n",
       "      <th>sp500</th>\n",
       "      <th>vix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>13.386595</td>\n",
       "      <td>0.219882</td>\n",
       "      <td>-0.318452</td>\n",
       "      <td>0.117500</td>\n",
       "      <td>-0.417633</td>\n",
       "      <td>0.122654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-10</td>\n",
       "      <td>13.624218</td>\n",
       "      <td>0.219882</td>\n",
       "      <td>-0.194838</td>\n",
       "      <td>0.168794</td>\n",
       "      <td>-0.425480</td>\n",
       "      <td>0.110445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-17</td>\n",
       "      <td>13.398741</td>\n",
       "      <td>0.236143</td>\n",
       "      <td>-0.292477</td>\n",
       "      <td>0.117500</td>\n",
       "      <td>-0.465229</td>\n",
       "      <td>0.532339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-24</td>\n",
       "      <td>13.137549</td>\n",
       "      <td>0.203353</td>\n",
       "      <td>-0.194838</td>\n",
       "      <td>0.106918</td>\n",
       "      <td>-0.481751</td>\n",
       "      <td>0.428645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>13.196760</td>\n",
       "      <td>0.134360</td>\n",
       "      <td>-0.242466</td>\n",
       "      <td>0.074483</td>\n",
       "      <td>-0.488929</td>\n",
       "      <td>0.487404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
       "\n",
       "        vix  \n",
       "0  0.122654  \n",
       "1  0.110445  \n",
       "2  0.532339  \n",
       "3  0.428645  \n",
       "4  0.487404  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train / Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:25.004750Z",
     "start_time": "2021-08-14T22:30:24.959917Z"
    }
   },
   "outputs": [],
   "source": [
    "test_size = 52\n",
    "\n",
    "train_df = df[:-test_size]\n",
    "test_df = df[-test_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:25.051746Z",
     "start_time": "2021-08-14T22:30:25.007107Z"
    }
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>claims</th>\n",
       "      <th>trend.unemploy</th>\n",
       "      <th>trend.filling</th>\n",
       "      <th>trend.job</th>\n",
       "      <th>sp500</th>\n",
       "      <th>vix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>13.386595</td>\n",
       "      <td>0.219882</td>\n",
       "      <td>-0.318452</td>\n",
       "      <td>0.117500</td>\n",
       "      <td>-0.417633</td>\n",
       "      <td>0.122654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-10</td>\n",
       "      <td>13.624218</td>\n",
       "      <td>0.219882</td>\n",
       "      <td>-0.194838</td>\n",
       "      <td>0.168794</td>\n",
       "      <td>-0.425480</td>\n",
       "      <td>0.110445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-17</td>\n",
       "      <td>13.398741</td>\n",
       "      <td>0.236143</td>\n",
       "      <td>-0.292477</td>\n",
       "      <td>0.117500</td>\n",
       "      <td>-0.465229</td>\n",
       "      <td>0.532339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-24</td>\n",
       "      <td>13.137549</td>\n",
       "      <td>0.203353</td>\n",
       "      <td>-0.194838</td>\n",
       "      <td>0.106918</td>\n",
       "      <td>-0.481751</td>\n",
       "      <td>0.428645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-31</td>\n",
       "      <td>13.196760</td>\n",
       "      <td>0.134360</td>\n",
       "      <td>-0.242466</td>\n",
       "      <td>0.074483</td>\n",
       "      <td>-0.488929</td>\n",
       "      <td>0.487404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        week     claims  trend.unemploy  trend.filling  trend.job     sp500  \\\n",
       "0 2010-01-03  13.386595        0.219882      -0.318452   0.117500 -0.417633   \n",
       "1 2010-01-10  13.624218        0.219882      -0.194838   0.168794 -0.425480   \n",
       "2 2010-01-17  13.398741        0.236143      -0.292477   0.117500 -0.465229   \n",
       "3 2010-01-24  13.137549        0.203353      -0.194838   0.106918 -0.481751   \n",
       "4 2010-01-31  13.196760        0.134360      -0.242466   0.074483 -0.488929   \n",
       "\n",
       "        vix  \n",
       "0  0.122654  \n",
       "1  0.110445  \n",
       "2  0.532339  \n",
       "3  0.428645  \n",
       "4  0.487404  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LGT Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In orbit, we have three types of LGT models, `LGTMAP`, `LGTAggregated` and  `LGTFull`.\n",
    "Orbit follows the sklearn model API. We can create an instance of the Orbit class and then call its fit and predict methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LGT-MAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGT model for MAP (Maximum a Posteriori) prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:25.087141Z",
     "start_time": "2021-08-14T22:30:25.053810Z"
    }
   },
   "outputs": [],
   "source": [
    "lgt = LGT(response_col=RESPONSE_COL,\n",
    "          date_col=DATE_COL,\n",
    "          regressor_col=['sp500'],\n",
    "          seasonality=52,\n",
    "          estimator='stan-map',\n",
    "          seed=8888)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:25.436164Z",
     "start_time": "2021-08-14T22:30:25.090790Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 122 ms, sys: 8.66 ms, total: 131 ms\n",
      "Wall time: 311 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lgt.fit(df=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:25.478598Z",
     "start_time": "2021-08-14T22:30:25.438743Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regressor</th>\n",
       "      <th>regressor_sign</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sp500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.12971</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  regressor regressor_sign  coefficient\n",
       "0     sp500        Regular     -0.12971"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.get_regression_coefs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:25.556377Z",
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>prediction</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-07-02</td>\n",
       "      <td>12.418269</td>\n",
       "      <td>12.469615</td>\n",
       "      <td>-0.008168</td>\n",
       "      <td>-0.043179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-07-09</td>\n",
       "      <td>12.536054</td>\n",
       "      <td>12.466527</td>\n",
       "      <td>0.114516</td>\n",
       "      <td>-0.044989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-16</td>\n",
       "      <td>12.376226</td>\n",
       "      <td>12.464701</td>\n",
       "      <td>-0.042788</td>\n",
       "      <td>-0.045687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-23</td>\n",
       "      <td>12.230214</td>\n",
       "      <td>12.463012</td>\n",
       "      <td>-0.187133</td>\n",
       "      <td>-0.045664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-07-30</td>\n",
       "      <td>12.181999</td>\n",
       "      <td>12.461337</td>\n",
       "      <td>-0.233425</td>\n",
       "      <td>-0.045912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  prediction      trend  seasonality  regression\n",
       "0 2017-07-02   12.418269  12.469615    -0.008168   -0.043179\n",
       "1 2017-07-09   12.536054  12.466527     0.114516   -0.044989\n",
       "2 2017-07-16   12.376226  12.464701    -0.042788   -0.045687\n",
       "3 2017-07-23   12.230214  12.463012    -0.187133   -0.045664\n",
       "4 2017-07-30   12.181999  12.461337    -0.233425   -0.045912"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
    "predicted_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_predicted_data(training_actual_df=train_df,\n",
    "                        predicted_df=predicted_df, \n",
    "                        date_col=DATE_COL,\n",
    "                        actual_col=RESPONSE_COL,  \n",
    "                        test_actual_df=test_df,\n",
    "                        title='Prediction with LGTMAP Model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LGT-MCMC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LGT model for full prediction. In full prediction, the prediction occurs as a function of each parameter posterior sample, and the prediction results are aggregated after prediction. Prediction will always return the median (aka 50th percentile) along with any additional percentiles that are specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:26.022451Z",
     "start_time": "2021-08-14T22:30:25.984217Z"
    }
   },
   "outputs": [],
   "source": [
    "lgt = LGT(response_col=RESPONSE_COL,\n",
    "          date_col=DATE_COL,\n",
    "          estimator='stan-mcmc',\n",
    "          regressor_col=['sp500'],\n",
    "          seasonality=52,\n",
    "          seed=8888)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:33.334912Z",
     "start_time": "2021-08-14T22:30:26.024607Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
      "WARNING:pystan:1 of 100 iterations ended with a divergence (1 %).\n",
      "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 74.3 ms, sys: 60.2 ms, total: 134 ms\n",
      "Wall time: 7.27 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lgt.fit(df=train_df, point_method=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:33.395867Z",
     "start_time": "2021-08-14T22:30:33.338277Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regressor</th>\n",
       "      <th>regressor_sign</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sp500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.105232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  regressor regressor_sign  coefficient\n",
       "0     sp500        Regular    -0.105232"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.get_regression_coefs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:33.500530Z",
     "start_time": "2021-08-14T22:30:33.398316Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>prediction_5</th>\n",
       "      <th>prediction</th>\n",
       "      <th>prediction_95</th>\n",
       "      <th>trend_5</th>\n",
       "      <th>trend</th>\n",
       "      <th>trend_95</th>\n",
       "      <th>seasonality_5</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>seasonality_95</th>\n",
       "      <th>regression_5</th>\n",
       "      <th>regression</th>\n",
       "      <th>regression_95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>12.121450</td>\n",
       "      <td>12.222408</td>\n",
       "      <td>12.336262</td>\n",
       "      <td>12.265188</td>\n",
       "      <td>12.421705</td>\n",
       "      <td>12.585555</td>\n",
       "      <td>-0.267771</td>\n",
       "      <td>-0.118234</td>\n",
       "      <td>-0.091846</td>\n",
       "      <td>-0.102139</td>\n",
       "      <td>-0.047667</td>\n",
       "      <td>-0.006838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>12.035511</td>\n",
       "      <td>12.164595</td>\n",
       "      <td>12.277020</td>\n",
       "      <td>12.287317</td>\n",
       "      <td>12.419028</td>\n",
       "      <td>12.548928</td>\n",
       "      <td>-0.322052</td>\n",
       "      <td>-0.184279</td>\n",
       "      <td>-0.148801</td>\n",
       "      <td>-0.105771</td>\n",
       "      <td>-0.049362</td>\n",
       "      <td>-0.007081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>12.157590</td>\n",
       "      <td>12.269862</td>\n",
       "      <td>12.398718</td>\n",
       "      <td>12.276035</td>\n",
       "      <td>12.420867</td>\n",
       "      <td>12.559491</td>\n",
       "      <td>-0.229390</td>\n",
       "      <td>-0.087272</td>\n",
       "      <td>-0.052992</td>\n",
       "      <td>-0.105822</td>\n",
       "      <td>-0.049386</td>\n",
       "      <td>-0.007084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>12.107162</td>\n",
       "      <td>12.253584</td>\n",
       "      <td>12.372341</td>\n",
       "      <td>12.276023</td>\n",
       "      <td>12.433750</td>\n",
       "      <td>12.578969</td>\n",
       "      <td>-0.259427</td>\n",
       "      <td>-0.118373</td>\n",
       "      <td>-0.082977</td>\n",
       "      <td>-0.103803</td>\n",
       "      <td>-0.048444</td>\n",
       "      <td>-0.006949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>12.139809</td>\n",
       "      <td>12.277902</td>\n",
       "      <td>12.380484</td>\n",
       "      <td>12.268771</td>\n",
       "      <td>12.408450</td>\n",
       "      <td>12.564300</td>\n",
       "      <td>-0.210829</td>\n",
       "      <td>-0.070608</td>\n",
       "      <td>-0.034148</td>\n",
       "      <td>-0.100795</td>\n",
       "      <td>-0.047040</td>\n",
       "      <td>-0.006748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         week  prediction_5  prediction  prediction_95    trend_5      trend  \\\n",
       "47 2018-05-27     12.121450   12.222408      12.336262  12.265188  12.421705   \n",
       "48 2018-06-03     12.035511   12.164595      12.277020  12.287317  12.419028   \n",
       "49 2018-06-10     12.157590   12.269862      12.398718  12.276035  12.420867   \n",
       "50 2018-06-17     12.107162   12.253584      12.372341  12.276023  12.433750   \n",
       "51 2018-06-24     12.139809   12.277902      12.380484  12.268771  12.408450   \n",
       "\n",
       "     trend_95  seasonality_5  seasonality  seasonality_95  regression_5  \\\n",
       "47  12.585555      -0.267771    -0.118234       -0.091846     -0.102139   \n",
       "48  12.548928      -0.322052    -0.184279       -0.148801     -0.105771   \n",
       "49  12.559491      -0.229390    -0.087272       -0.052992     -0.105822   \n",
       "50  12.578969      -0.259427    -0.118373       -0.082977     -0.103803   \n",
       "51  12.564300      -0.210829    -0.070608       -0.034148     -0.100795   \n",
       "\n",
       "    regression  regression_95  \n",
       "47   -0.047667      -0.006838  \n",
       "48   -0.049362      -0.007081  \n",
       "49   -0.049386      -0.007084  \n",
       "50   -0.048444      -0.006949  \n",
       "51   -0.047040      -0.006748  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
    "predicted_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:33.846496Z",
     "start_time": "2021-08-14T22:30:33.502243Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_predicted_data(training_actual_df=train_df,\n",
    "                        predicted_df=predicted_df,\n",
    "                        date_col=DATE_COL,\n",
    "                        actual_col=RESPONSE_COL,\n",
    "                        test_actual_df=test_df,\n",
    "                        title='Prediction with LGTFull Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:41.850509Z",
     "start_time": "2021-08-14T22:30:33.848052Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n",
      "WARNING:pystan:1 of 100 iterations ended with a divergence (1 %).\n",
      "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>prediction</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>12.213349</td>\n",
       "      <td>12.400611</td>\n",
       "      <td>-0.142465</td>\n",
       "      <td>-0.044797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>12.147506</td>\n",
       "      <td>12.398705</td>\n",
       "      <td>-0.204809</td>\n",
       "      <td>-0.046390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>12.241098</td>\n",
       "      <td>12.396799</td>\n",
       "      <td>-0.109288</td>\n",
       "      <td>-0.046413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>12.209391</td>\n",
       "      <td>12.394893</td>\n",
       "      <td>-0.139975</td>\n",
       "      <td>-0.045527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>12.257019</td>\n",
       "      <td>12.392987</td>\n",
       "      <td>-0.091761</td>\n",
       "      <td>-0.044208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         week  prediction      trend  seasonality  regression\n",
       "47 2018-05-27   12.213349  12.400611    -0.142465   -0.044797\n",
       "48 2018-06-03   12.147506  12.398705    -0.204809   -0.046390\n",
       "49 2018-06-10   12.241098  12.396799    -0.109288   -0.046413\n",
       "50 2018-06-17   12.209391  12.394893    -0.139975   -0.045527\n",
       "51 2018-06-24   12.257019  12.392987    -0.091761   -0.044208"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.fit(df=train_df, point_method='mean')\n",
    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
    "predicted_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:41.900678Z",
     "start_time": "2021-08-14T22:30:41.852899Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regressor</th>\n",
       "      <th>regressor_sign</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sp500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.098896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  regressor regressor_sign  coefficient\n",
       "0     sp500        Regular    -0.098896"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.get_regression_coefs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:49.197998Z",
     "start_time": "2021-08-14T22:30:41.903285Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:pystan:Maximum (flat) parameter count (1000) exceeded: skipping diagnostic tests for n_eff and Rhat.\n",
      "To run all diagnostics call pystan.check_hmc_diagnostics(fit)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>prediction</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>12.232460</td>\n",
       "      <td>12.398649</td>\n",
       "      <td>-0.119574</td>\n",
       "      <td>-0.046614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>12.170546</td>\n",
       "      <td>12.396948</td>\n",
       "      <td>-0.178130</td>\n",
       "      <td>-0.048272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>12.261167</td>\n",
       "      <td>12.395247</td>\n",
       "      <td>-0.085784</td>\n",
       "      <td>-0.048296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>12.229913</td>\n",
       "      <td>12.393546</td>\n",
       "      <td>-0.116259</td>\n",
       "      <td>-0.047374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>12.277632</td>\n",
       "      <td>12.391845</td>\n",
       "      <td>-0.068211</td>\n",
       "      <td>-0.046001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         week  prediction      trend  seasonality  regression\n",
       "47 2018-05-27   12.232460  12.398649    -0.119574   -0.046614\n",
       "48 2018-06-03   12.170546  12.396948    -0.178130   -0.048272\n",
       "49 2018-06-10   12.261167  12.395247    -0.085784   -0.048296\n",
       "50 2018-06-17   12.229913  12.393546    -0.116259   -0.047374\n",
       "51 2018-06-24   12.277632  12.391845    -0.068211   -0.046001"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.fit(df=train_df, point_method='median')\n",
    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
    "predicted_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:49.246426Z",
     "start_time": "2021-08-14T22:30:49.200243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regressor</th>\n",
       "      <th>regressor_sign</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sp500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.102908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  regressor regressor_sign  coefficient\n",
       "0     sp500        Regular    -0.102908"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.get_regression_coefs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LGT-SVI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:30:49.281899Z",
     "start_time": "2021-08-14T22:30:49.248454Z"
    }
   },
   "outputs": [],
   "source": [
    "lgt = LGT(response_col=RESPONSE_COL,\n",
    "          date_col=DATE_COL,\n",
    "          estimator='pyro-svi',\n",
    "          regressor_col=['sp500'],\n",
    "          seasonality=52,\n",
    "          seed=8888,\n",
    "          num_steps=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:31:08.077798Z",
     "start_time": "2021-08-14T22:30:49.284111Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Guessed max_plate_nesting = 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.8 s, sys: 1.23 s, total: 20 s\n",
      "Wall time: 18.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lgt.fit(df=train_df, point_method=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:31:08.119495Z",
     "start_time": "2021-08-14T22:31:08.083035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regressor</th>\n",
       "      <th>regressor_sign</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sp500</td>\n",
       "      <td>Regular</td>\n",
       "      <td>-0.079233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  regressor regressor_sign  coefficient\n",
       "0     sp500        Regular    -0.079233"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.get_regression_coefs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:31:08.208427Z",
     "start_time": "2021-08-14T22:31:08.122781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>prediction_5</th>\n",
       "      <th>prediction</th>\n",
       "      <th>prediction_95</th>\n",
       "      <th>trend_5</th>\n",
       "      <th>trend</th>\n",
       "      <th>trend_95</th>\n",
       "      <th>seasonality_5</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>seasonality_95</th>\n",
       "      <th>regression_5</th>\n",
       "      <th>regression</th>\n",
       "      <th>regression_95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>12.134188</td>\n",
       "      <td>12.239194</td>\n",
       "      <td>12.345663</td>\n",
       "      <td>12.293881</td>\n",
       "      <td>12.433754</td>\n",
       "      <td>12.555211</td>\n",
       "      <td>-0.226589</td>\n",
       "      <td>-0.153846</td>\n",
       "      <td>-0.110348</td>\n",
       "      <td>-0.080082</td>\n",
       "      <td>-0.035890</td>\n",
       "      <td>0.017217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>12.067673</td>\n",
       "      <td>12.174843</td>\n",
       "      <td>12.315627</td>\n",
       "      <td>12.303840</td>\n",
       "      <td>12.436522</td>\n",
       "      <td>12.590489</td>\n",
       "      <td>-0.289718</td>\n",
       "      <td>-0.216006</td>\n",
       "      <td>-0.170248</td>\n",
       "      <td>-0.082930</td>\n",
       "      <td>-0.037167</td>\n",
       "      <td>0.017829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>12.151177</td>\n",
       "      <td>12.274897</td>\n",
       "      <td>12.367586</td>\n",
       "      <td>12.302466</td>\n",
       "      <td>12.445126</td>\n",
       "      <td>12.549077</td>\n",
       "      <td>-0.193503</td>\n",
       "      <td>-0.119538</td>\n",
       "      <td>-0.078269</td>\n",
       "      <td>-0.082970</td>\n",
       "      <td>-0.037185</td>\n",
       "      <td>0.017838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>12.126432</td>\n",
       "      <td>12.247311</td>\n",
       "      <td>12.345989</td>\n",
       "      <td>12.310941</td>\n",
       "      <td>12.447040</td>\n",
       "      <td>12.554084</td>\n",
       "      <td>-0.223979</td>\n",
       "      <td>-0.150387</td>\n",
       "      <td>-0.109883</td>\n",
       "      <td>-0.081387</td>\n",
       "      <td>-0.036475</td>\n",
       "      <td>0.017497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>12.156756</td>\n",
       "      <td>12.296717</td>\n",
       "      <td>12.396728</td>\n",
       "      <td>12.298664</td>\n",
       "      <td>12.426746</td>\n",
       "      <td>12.567291</td>\n",
       "      <td>-0.175204</td>\n",
       "      <td>-0.102026</td>\n",
       "      <td>-0.061579</td>\n",
       "      <td>-0.079028</td>\n",
       "      <td>-0.035418</td>\n",
       "      <td>0.016990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         week  prediction_5  prediction  prediction_95    trend_5      trend  \\\n",
       "47 2018-05-27     12.134188   12.239194      12.345663  12.293881  12.433754   \n",
       "48 2018-06-03     12.067673   12.174843      12.315627  12.303840  12.436522   \n",
       "49 2018-06-10     12.151177   12.274897      12.367586  12.302466  12.445126   \n",
       "50 2018-06-17     12.126432   12.247311      12.345989  12.310941  12.447040   \n",
       "51 2018-06-24     12.156756   12.296717      12.396728  12.298664  12.426746   \n",
       "\n",
       "     trend_95  seasonality_5  seasonality  seasonality_95  regression_5  \\\n",
       "47  12.555211      -0.226589    -0.153846       -0.110348     -0.080082   \n",
       "48  12.590489      -0.289718    -0.216006       -0.170248     -0.082930   \n",
       "49  12.549077      -0.193503    -0.119538       -0.078269     -0.082970   \n",
       "50  12.554084      -0.223979    -0.150387       -0.109883     -0.081387   \n",
       "51  12.567291      -0.175204    -0.102026       -0.061579     -0.079028   \n",
       "\n",
       "    regression  regression_95  \n",
       "47   -0.035890       0.017217  \n",
       "48   -0.037167       0.017829  \n",
       "49   -0.037185       0.017838  \n",
       "50   -0.036475       0.017497  \n",
       "51   -0.035418       0.016990  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_df = lgt.predict(df=test_df, point_method=None, decompose=True)\n",
    "predicted_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:31:08.554651Z",
     "start_time": "2021-08-14T22:31:08.210752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plot_predicted_data(training_actual_df=train_df,\n",
    "                        predicted_df=predicted_df,\n",
    "                        date_col=DATE_COL,\n",
    "                        actual_col=RESPONSE_COL,\n",
    "                        test_actual_df=test_df,\n",
    "                        title='Prediction with LGTFull Model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:31:27.351013Z",
     "start_time": "2021-08-14T22:31:08.556464Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Guessed max_plate_nesting = 2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>prediction</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>12.238239</td>\n",
       "      <td>12.431259</td>\n",
       "      <td>-0.160223</td>\n",
       "      <td>-0.032796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>12.175089</td>\n",
       "      <td>12.429819</td>\n",
       "      <td>-0.220767</td>\n",
       "      <td>-0.033963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>12.268645</td>\n",
       "      <td>12.428380</td>\n",
       "      <td>-0.125755</td>\n",
       "      <td>-0.033979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>12.237186</td>\n",
       "      <td>12.426940</td>\n",
       "      <td>-0.156423</td>\n",
       "      <td>-0.033331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>12.285014</td>\n",
       "      <td>12.425500</td>\n",
       "      <td>-0.108122</td>\n",
       "      <td>-0.032365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         week  prediction      trend  seasonality  regression\n",
       "47 2018-05-27   12.238239  12.431259    -0.160223   -0.032796\n",
       "48 2018-06-03   12.175089  12.429819    -0.220767   -0.033963\n",
       "49 2018-06-10   12.268645  12.428380    -0.125755   -0.033979\n",
       "50 2018-06-17   12.237186  12.426940    -0.156423   -0.033331\n",
       "51 2018-06-24   12.285014  12.425500    -0.108122   -0.032365"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.fit(df=train_df, point_method='mean')\n",
    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
    "predicted_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-14T22:31:46.496066Z",
     "start_time": "2021-08-14T22:31:27.352760Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Guessed max_plate_nesting = 2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
       "      <th>prediction</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonality</th>\n",
       "      <th>regression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2018-05-27</td>\n",
       "      <td>12.242491</td>\n",
       "      <td>12.432228</td>\n",
       "      <td>-0.153846</td>\n",
       "      <td>-0.035890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2018-06-03</td>\n",
       "      <td>12.177768</td>\n",
       "      <td>12.430941</td>\n",
       "      <td>-0.216006</td>\n",
       "      <td>-0.037167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2018-06-10</td>\n",
       "      <td>12.272932</td>\n",
       "      <td>12.429655</td>\n",
       "      <td>-0.119538</td>\n",
       "      <td>-0.037185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2018-06-17</td>\n",
       "      <td>12.241506</td>\n",
       "      <td>12.428368</td>\n",
       "      <td>-0.150387</td>\n",
       "      <td>-0.036475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2018-06-24</td>\n",
       "      <td>12.289638</td>\n",
       "      <td>12.427082</td>\n",
       "      <td>-0.102026</td>\n",
       "      <td>-0.035418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         week  prediction      trend  seasonality  regression\n",
       "47 2018-05-27   12.242491  12.432228    -0.153846   -0.035890\n",
       "48 2018-06-03   12.177768  12.430941    -0.216006   -0.037167\n",
       "49 2018-06-10   12.272932  12.429655    -0.119538   -0.037185\n",
       "50 2018-06-17   12.241506  12.428368    -0.150387   -0.036475\n",
       "51 2018-06-24   12.289638  12.427082    -0.102026   -0.035418"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgt.fit(df=train_df, point_method='median')\n",
    "predicted_df = lgt.predict(df=test_df, decompose=True)\n",
    "predicted_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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